The \(j^{\textit {th}}\)  type of evidence has a neural encoder \(f_j\) . An individual piece of evidence \(\mathsf {X}\)  is either encoded as a single vector or as a set of vectors with no particular ordering. For example, our implementation encodes Javadoc comments as vectors using LSTMs, and each member of a set of formal parameters using a basic feedforward network.
Let \(\mathsf {X}_{j,k}\)  refer to the \(k^{\textit {th}}\)  instance of the \(j^{\textit {th}}\)  kind of evidence in \(\mathsf {X}\) .
Assume a Normal prior on \({Z}\) , and let
\(\mathcal {P}(\mathsf {X}| \mathsf {Z}) = \prod \limits _{j,k} \mathcal {N} \left(f_{j}(\mathsf {X}_{j,k}) \mid \mathsf {Z}, \textbf {I} \sigma ^2_j\right)\) .
Assume that the encoding of each type of evidence is sampled from a Normal centered at \(\mathsf {Z}\) .
If \(f\)  is 1-1 and onto,
we have [1]}:
\( \nonumber \mathcal {P}(\mathsf {Z}| \mathsf {X}) = \mathcal {N} \left(\mathsf {Z}\mid \frac{\sum \limits _{j,k} \sigma ^{-2}_j f_{j} (\mathsf {X}_{j,k})}{1 + \sum \limits _j |\mathsf {X}_{j}| \sigma ^{-2}_j}, \frac{1}{1 + \sum \limits _j |\mathsf {X}_{j}| \sigma ^{-2}_j}\textbf {I}\right)\) 
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